[IEEE-bhpjobs] Wednesday, September 14th, 11:00 am

[Walter Heger]

Research Initiation Project Proposal Talk: Mason Matthews

Title: Co-tracking: Employing Co-training as a Means to Capture Concept
Drift
Time: Wednesday, September 14th, 11:00 am
Place: LSRC D344

Advisor: Carlo Tomasi
Committee: Ron Parr, Lawrence Carin

Abstract:
When solving supervised classification problems, it is typically
assumed
that the training set and the test set are drawn from identical
distributions.  However, in many applications, the distributions can
change over time, rendering the initial classification function
determined from the training set useless.  Consider the problem of head
tracking: positive images may be limited to frontal faces at first, but
as the heads rotate, the space of positive images changes.  In order to
maintain accurate classification functions despite changing
distributions, the paradigm of concept drift was developed.

While concept drift methods have been proven effective under the proper
circumstances, they assume that labeled data is provided at each
timestep.  This is not a reasonable expectation in vision problems such
as head tracking, but there are a number of semi-supervised learning
algorithms which may act as substitutes.  Co-training is one such
algorithm: given two independent classifiers on the same data,
co-training allows them to teach each other by providing labels for new
data points.

This project aims to develop a modified co-training system which can be
used to track objects in video that evolve over time.  Concept drift,
co-training, and the theoretical framework for such a system are
discussed in this proposal, and a head tracking algorithm is proposed.
Preliminary results on simulated and real-world heads are also
presented.

--
Mason F. Matthews
Duke University
Department of Computer Science
LSRC D208
(919) 660-4008

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